Everyday experts derive hypotheses about protein function and structure from looking at patterns in protein families. The hope is to speculate whether protein X binds to Y, and if so, where. Two published methods address the goal of predicting protein interactions from sequence. The first simply uses a particular region in the hydrophobic moment plot to suggest binding interfaces; it predicts too many putative interfaces (66% of all residues). The second predicts interfaces from the co-occurrence of sequence motifs and domains, i.e. by using existing experimental information in a clever way. Obviously, this method cannot predict novel interactions. Here, we propose to develop methods predicting interface segments, i.e. regions of residues consecutive in sequence that are in contact with other interface segments. We propose separate methods for internal and external interfaces. We have recently shown that internal, chain-chain, and protein-protein interface segments differ - on average. Thus, our first aim is to predict the interface type based on a combination of sequence features and predicted properties (secondary structure, accessibility, and post-translational modifications). Such a prediction will already provide the first hint for functional regions, and it will simplify the next step. The second aim will be to develop a system that suggests possible protein-protein interaction partners, as well, as putative oligomer interfaces. The third goal will be to develop new methods that predict distances between internal interfaces. This final step will hopefully complement the prediction of external interfaces and will assist methods predicting protein structure. The basic means explored will be combinations of statistics and neural networks using multiple sequence alignments. The goal is a low-resolution prediction succeeding often enough to distinguish between internal and external interfaces to assist the design of experiments in molecular and medical biology. In the worst case, we anticipate to develop novel techniques that will enable experimentalists to correctly identify the most likely protein-protein interaction segments most of the time. In the best case, we hope to introduce methods that allow automatic discoveries for entire proteomes and considerably help structure prediction.